The goals of this exercise were twofold: introduce students to scientific programming languages and reinforce hydrological concepts through an assignment that utilized a publically available high-frequency dataset. Although analysis of environmental data is almost always performed using MATLAB, R, or something similar, students often lack opportunities to become comfortable with their use in a setting that provides instructional support. Additionally, although hydrology and other environmental sciences are vitally dependent on high-quality, real-world data, the instruction of these subjects often favors conceptual lessons at the expense of exposure to actual environmental data. Therefore, our assignment is designed to introduce or reinforce scientific programming languages to the students while leveraging a real-world dataset to reinforce concepts learned in class and familiarize students with some of the challenges unique to large, environmental datasets. The students acquired data, imported it into Matlab or R, performed analyses on it, and exported the modified dataset to a common format to be shared along with their script.

3. Reinforce conceptual understanding of water and energy fluxes through interpretation of real-world data

Calculate components of the energy balance and discuss temporal dynamics with respect to site specific environmental conditions

Calculate evapotranspiration and potential evapotranspiration using common methods and discuss assumptions associated with each

Data Source

The AmeriFlux network measures carbon, water, and energy flux at the ecosystem level across North and South America. These measurements are used to build an understanding of fluxes of energy, water, and nutrients from ecosystems across the western hemisphere and to evaluate ecosystem response to landuse and climate change. This project is funded by the US Department of Energy to encourage consistent measurements and long-term monitoring of these ecosystem fluxes. Data are collected at individual research sites and uploaded to a central Ameriflux server, from which researchers and educators can download individual data records. The network consists of 110 active research sites, of which 44 are designated “core” sites, which maintain specific and high standards of data collection. www.ameriflux.lbl.gov

Concluding Remarks

We assessed students’ experience with programming (in any language) using a pre-assignment survey. We found that half of the students had no experience with coding, and of those who did have experience only two rated themselves as proficient. We provided two lab sessions where we introduced students to the programming platforms and were on hand to answer specific questions related to the assignment, and then extended office hours to help along the way. In doing so, we found that no students were unable to get the help they needed on the assignment. In a post-assignment questionnaire we found that over half of the class rated themselves as having moderate experience with coding (over 3 on a scale from 1-5), and none of the students rated themselves lower than 2.

However, this method of intensive instructional support required significant time commitments from both instructors. In the future, requiring students to watch tutorials online, and to gradually introduce them to the programs throughout the semester would help get students past the initial learning curve. We found that students did a good job of working through coding issues and questions in small groups. Creating opportunities for them to do this (e.g., booking a computer lab for them, assigning or encouraging formalized groups, etc) would be beneficial.

Related Projects

United Nations Sustainable Development Goal 7 calls for universal access to affordable, reliable, sustainable, and modern energy. Researchers and practitioners around the world have responded to this call by producing a wealth of energy access data. While many data gaps still exist, are we capturing the fullest potential from the information and research we do have, and what it tells us about how to accelerate energy access? Power for All’s Platform for Energy Access Knowledge (PEAK) is an interactive knowledge platform designed to automatically curate, organize, and streamline large, growing bodies of data into digestible, sharable, and useable knowledge through automated data capture, indexing, and visualization. A team of students led by Rebekah Shirley will consult with Power for All to creatively visualize PEAK’s library, and to explore machine learning and natural language processing tools that can enable auto-extraction and visualization of data for more effective science communication.

A team of students led by researchers in the Energy Data Analytics Lab and the Sustainable Energy Transitions Initiative will develop machine learning techniques for automatically mapping global electricity infrastructure using satellite imagery. By identifying substations, transmission lines, and distribution lines, students will create and publish a training dataset that we will use to automate grid infrastructure geolocation. These data and techniques will empower researchers and policymakers to better understand who has grid-connected access to electricity, who is underserved, and how to most efficiently transition communities and countries towards sustainable electrification.